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Objectives Studies have demonstrated immune dysfunction in adolescents with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS); however, evidence is varied. The current study used network analysis to examine relationships between cytokines among a sample of pediatric participants with ME/CFS. Methods 10,119 youth aged 5–17 in the Chicagoland area were screened for ME/CFS; 111 subjects and controls were brought in for a physician examination and completed a blood draw. Youth were classified as controls (Cs, N = 43), ME/CFS (N = 23) or severe (S-ME/CFS, N = 45). Patterns of plasma cytokine networks were analyzed. Results All participant groups displayed a primary network of interconnected cytokines. In the ME/CFS group, inflammatory cytokines IL-12p70, IL-17A, and IFN-γ were connected and included in the primary membership, suggesting activation of inflammatory mechanisms. The S-ME/CFS group demonstrated a strong relationship between IL-17A and IL-23, a connection associated with chronic inflammation. The relationships of IL-6 and IL-8 in ME/CFS and S-ME/CFS participants also differed from Cs. Together, these results indicate pro-inflammatory responses in our illness populations. Discussion Our data imply biological differences between our three participant groups, with ME/CFS and S-ME/CFS participants demonstrating an inflammatory profile. Examining co-expression of cytokines may aid in the identification of a biomarker for pediatric ME/CFS.

INTRODUCTIONNepal due to its geographical condition, inadequate resuscitation on time, and lack of full-time intensivist in a trauma team is a cause of trauma-related morbidity and mortality. MATERIAL AND METHODSIt was a prospective, descriptive observational cross-sectional study in a level three intensive care unit of National Medical College for six month. All patients >18 years with a history of road traffic accidents, falls, drowning, physical assaults, and self-inflicted violence admitted to the mixed intensive care unit of a tertiary care hospital were included in this study. RESULTSOne hundred and ninety-one patients were included in this study. 152(79.5%) were males and 39(20.4%) were females. A road traffic accident was the most common mode of injury in 166(86.9%) patients. 178(93.1%) patients survived and went home, 10(5.3%) expired, 2(1%) left the hospital against medical advice (LAMA) and 1(0.5%) gave do not resuscitate orders (DNR). The mean days on the mechanical ventilator was 3.39±2.57 days. Mortality in an intubated patient was 8.6%. Mean LOS in the ICU was 4.28±4.12 days. SOFA and APACHE II scores were a good predictor of outcome, with the SOFA score being the most effective Injury severity score predicted mortality with a sensitivity of 88.9%, a specificity of 22%, with an AUROC curve of 0.543±0.092 with cut off score of 5.5. CONCLUSIONRaising public awareness, and strictly following the traffic rules can help in reducing the incidence of trauma. Trauma team consisting of full-time intensivists should be established to decrease the mortality in the intensive care unit.

This study aimed to contribute to the development of a research case definition for post-acute sequelae of SARS CoV-2 infection (PASC) using a PASC data set and experiences from case definitions developed for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Our database included patients with PASC who provided self-report symptomology during the onset of infection and the time of survey completion (post-infection). We found that we could distinguish between those with mild, moderate, and severe PASC. Regarding the proportion meeting an ME/CFS case definition, we found 0% in the mildly impaired group, 30.6% to 62.6% in the moderately impaired group, and 74.3% to 89.0% in the severely impaired group. Based on these preliminary data, we propose a 5-part classification system for PASC. Axis 1 involves the variant of the COVID infection and the type of documentation of the infection. Axis 2 involves the time elapsed since infection. Axis 3 involves the type of medical collateral damage to different organs. Axis 4 involves functional impairment classified into three categories: mild, moderate, or severe. Finally, Axis 5 is the identified symptoms. Finally, if the patient has been sick for 6 or more months, it is important to determine whether the person has met the ME/CFS criteria. This proposed 5-part classification system for PASC might bring considerable clarity to diagnosing PASC.

The Viscous-Plastic (VP) rheology with an elliptical yield curve and normal flow rule is implemented in a Lagrangian modelling framework using the Smoothed Particle Hydrodynamics (SPH) meshfree method. Results show, from perturbation analysis of SPH sea-ice dynamic equations, that the classical SPH particle density formulation expressed as a function of sea-ice concentration and mean ice thickness, leads to incorrect plastic wave speed. We propose a new formulation for particle density that gives a plastic wave speed in line with theory. In all cases, the plastic wave in the SPH framework is dispersive and depends on the smoothing length (i.e., the spatial resolution) and on the SPH kernel employed in contrast with its finite difference method (FDM) implementation counterpart. The steady-state solution for the simple 1D ridging experiment is in agreement with the analytical solution within an error of 1 %. SPH is also able to simulate a stable upstream ice arch in an idealized domain representing the Nares Strait in low wind regime (5.3 [m · s−1]) with an ellipse aspect ratio of 2, an average thickness of 1 [m] and free-slip boundary conditions in opposition to the FDM implementation that requires higher shear strength to simulate it. In higher wind regime (7.5 [m · s−1]) no stable ice arches are simulated — unless the thickness is increased — and the ice arch formation showed no dependence on the size of particles contrary to what is observed in the discrete element framework. Finally, the SPH framework is explicit, can take full advantage of parallel processing capabilities and show potential for pan-arctic climate simulations.

Numerical modelling of the Arctic ocean dynamics with real-time simulation capability is useful for designing, developing, testing, and validating the performance of Dynamically Positioned (DP) and Autonomous ships/offshore platforms. Advanced simulation technology needs to be developed to predict the expected loads on these systems due to the complex interactions with environmental disturbances. This paper presents models of waves, currents, wind, and ice that comply with the real-time simulation requirements and adequately capture the dynamic characteristics of the most relevant physical processes. A 3D dispersive numerical model is deployed to predict the wave parameters to be utilized to compute the wave loads on a ship with known Response Amplitude Operators (RAO). A uniform current load is then incorporated in a superposition manner by using a combined wave-current field dispersion relation capable of expressing the wavenumber of an interactive wave-current field. The mean and the gust wind components are added to the resultant force components. A multiple regression-based ice model is used to predict the loads caused by an ice field characterized by varied ice thickness, concentration, floe size, drift speed and directions. The stationkeeping performance of a generic DP-controlled ship subjected to environmental disturbances is evaluated for a range of environmental conditions. The proposed models can help design, develop, and evaluate dynamic positioning and autonomous ship controllers’ performance. Another application may be developing a realistic simulation environment to train conventional, DP-controlled and autonomous ship operators., 40th International Conference on Ocean, Offshore & Arctic Engineering, OMAE 2021, June 21-30, 2021, Virtual Conference, Online

Sea-ice observation and estimation of ice forces are becoming increasingly important with the increased activities in arctic water. Proper modelling of ice properties and forces is crucial in such cases to ensure safe operations, for example, Dynamic Positioning (DP). This work, therefore, aims at developing algorithms for image processing to extract useful ice properties and subsequently aid the modelling of ice load exerted on a floating platform or a ship. A robust algorithm capable of detecting closely connected and overlapped ice floes with various sizes and shapes is presented, which is the first step for accurate modelling of ice forces. To demonstrate the effectiveness of this approach, image frames processed from videos produced during an experiment using a model ship performed at the National Research Council’s Ocean Coastal and River Engineering Research Centre (NRC-OCRE) in Canada are used. Simulated ice floe images are also used to show the efficiency of the proposed model and compare it with other published work. The model will be extended further to extract and correlate other ice properties with the ice forces and develop a machine learning based ice force prediction model. The predicted force from that model will then be used as a feedforward to Dynamic Positioning (DP) controller with an aim to improve the performance of the controller.

Oral epithelial dysplasia (OED) is a precursor state usually preceding oral squamous cell carcinoma (OSCC). Histological grading is the current gold standard for OED prognostication but is subjective and variable with unreliable outcome prediction. We explore if individual OED histological features can be used to develop and evaluate prognostic models for malignant transformation and recurrence prediction. Digitised tissue slides for a cohort of 109 OED cases were reviewed by three expert pathologists, where the prevalence and agreement of architectural and cytological histological features was assessed and association with clinical outcomes analysed using Cox proportional hazards regression and Kaplan-Meier curves. Within the cohort, the most prevalent features were basal cell hyperplasia (72%) and irregular surface keratin (60%), and least common were verrucous surface (26%), loss of epithelial cohesion (30%), lymphocytic band and dyskeratosis (34%). Several features were significant for transformation (p

The silicon heterojunction solar cell (SHJ) is considered the dominant state-of-the-art silicon solar cell technology due to its excellent passivation quality and high efficiency. However, SHJ’s light management performance is limited by its narrow optical absorption in long-wave near-infrared (NIR) due to the front, and back tin-doped indium oxide (ITO) layer’s free carrier absorption and reflection losses. Despite the light-trapping efficiency (LTE) schemes adopted by SHJ in terms of back surface texturing, the previous investigations highlighted the ITO layer as a reason for an essential long-wavelength light loss mechanism in SHJ solar cells. In this study, we propose the use of Molybdenum disulfide (MoS2) as a way of improving back-reflection in SHJ. The text presents simulations of the optical response in the backside of the SHJ applying the Monte-Carlo raytracing method with a web-based Sunsolve high-precision raytracing tool. The solar cells’ electrical parameters were also resolved using the standard electrical equivalent circuit model provided by Sunsolve. The proposed structure geometry slightly improved the SHJ cell optical current density by ~0.37% (rel.), and hence efficiency (η) by about 0.4% (rel.). The SHJ cell efficiency improved by 21.68% after applying thinner back ITO of about 30 nm overlayed on ~1 nm MoS2. The efficiency improvement following the application of MoS2 is tentatively attributed to the increased NIR absorption in the silicon bulk due to the light constructive interface with the backside components, namely silver (Ag) and ITO. Study outcomes showed that improved SHJ efficiency could be further optimized by addressing front cell components, mainly front ITO and MoS2 contact engineering.

Cancer is the second leading cause of death worldwide. Breast cancer, the most common cancer found in women, affects 2.1 million women annually and has the highest number of cancer related deaths. The objective of the current meta-analysis is to evaluate the effects of post-diagnosis exercises on depression, physical functioning, and mortality in breast cancer survivors.The search for eligible articles was conducted through CINAHL, Medline/PubMed, Scopus, Cochrane, Emerald Insight and Web of Science, Embase database, MEDLINE In-Process, Elsevier, Google Scholar, PsycInfo, Cochrane Database of Systematic Reviews (CDSR), Cochrane Central Register of Controlled Trials (CENTRAL), Allied and Complementary Medicine (AMED), Biosis Previews, SPORTDiscus, PEDro scientific databases from 1974 to 2020. Following the exclusion procedure, 26 articles yielded for final analysis. The combined statistics for depression, physical functioning, and mortality in breast cancer survivors were calculated using standardized mean differences (SMD). Standard errors and 95% confidence intervals (CI) were converted to standard deviations as required. For mortality, combined statistics were calculated using hazard ratios (HR). The 95% CIs were converted to standard errors as required. The forest plots display point estimates and 95% CIs.Statistically significant improvements on levels of depression were identified following the exercise intervention, suggesting that post-diagnosis physical activity leads to a decrease in depression scores. Overall, post-diagnosis exercise led to a 37% reduction in the rate of breast cancer-specific mortality. The all-cause mortality rate was decreased by 39% with the inclusion of moderate physical activity as the part of daily routine.Future studies should look at how to improve the quality of life while incorporating physical activity as a daily routine after breast-cancer treatment.

Empirical models based on full-scale or model scale measurements are often preferred over high-fidelity numerical models when real-time predictions for ice actions on ships and floating offshore platforms are needed. This paper presents multiple regression-based empirical models for predicting a Dynamically Position (DP) controlled vessel and managed ice-field interactions developed using a large dataset from ice basin tests of two distinct DP-controlled vessels. The authors carried out a detailed analysis of measurements from two physical modelling programs for developing a unique database. A multiple regression modelling procedure was developed to predict the time-averaged and average peaks of forces and moment of the DP-controlled vessels due to ice actions in multiple configurations of managed ice-field. The models were validated using model scale measurements for the predictions of time-averaged and average peak responses. The models were then used to evaluate the effects of each ice-field parameter on vessel forces and moment and interaction event parameters. The models presented in this paper showed reasonable accuracy in predicting the effects of several ice-field parameters on the forces and moments of two distinct DP-controlled vessels in a range of realistic operating conditions.